Libraries
library(tidyverse)
Read data
df_ot <- read_csv("./data/state_of_industry_data.csv") %>%
pivot_longer( -c(Type, Name), names_to = "Date", values_to = "Value") %>%
mutate(Date = as.Date(Date, format="%m/%d"))
countries1 <- c("Germany", "United States", "United Kingdom", "Global")
countries2 <- c("Mexico", "Ireland", "Canada", "Australia")
df_ot <- df_ot %>% mutate(
Group = case_when(Name %in% countries1 ~ 1,
Name %in% countries2 ~ 2,
TRUE ~ 3))
updated_on <- df_ot$Date %>% max()
Plot by Country
plot <- df_ot %>% filter(Group %in% 1:2) %>%
ggplot(aes(x = Date, y = Value, color=Value)) +
geom_line(show.legend = F) + geom_point() +
theme(legend.position = "none",
axis.title=element_blank()) +
scale_y_continuous(breaks = seq(-100, 0, by = 20)) +
scale_x_date(breaks="1 week", minor_breaks = "day",date_labels = "%b %d") +
coord_cartesian(ylim=c(-120, 20)) +
labs(title = "Effect on Reastaurant Reservations by Country",
subtitle = paste0("Year-over-year Percentage Change (Updated on : ", updated_on, ")"),
caption = "Data source: https://www.opentable.com/state-of-industry") +
geom_text(aes(label = Value), nudge_y=-10, nudge_x = -1/3, size=3) +
facet_wrap(~Name, ncol=2)
print(plot)
Plot by US States
plot <- df_ot %>% filter(Group %in% 3) %>%
ggplot(aes(x = Date, y = Value, color=Value)) +
geom_line(show.legend = F) + geom_point() +
theme(legend.position = "none",
axis.title=element_blank()) +
scale_y_continuous(breaks = seq(-100, 0, by = 20)) +
scale_x_date(breaks="1 week", minor_breaks = "day",date_labels = "%b %d") +
coord_cartesian(ylim=c(-120, 20)) +
labs(title = "Effect on Reastaurant Reservations by US States",
subtitle = paste0("Year-over-year Percentage Change (Updated on : ", updated_on, ")"),
caption = "Data source: https://www.opentable.com/state-of-industry") +
geom_text(aes(label = Value), nudge_y=-13, nudge_x = -1/3, size=2.3) +
facet_wrap(~Name, ncol=4) +
theme(strip.background = element_blank(), strip.placement = "outside")
print(plot)
